Title :
Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm
Author :
Juang, Chia-Feng ; Hsu, Chao-Hsin
Author_Institution :
Dept. of Electr. Eng., Nat. Chung-Hsing Univ., Taichung, Taiwan
Abstract :
Online adaptive temperature control by field-programmable gate array (FPGA) - implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization (SPSO) is proposed. In SPSO, each RFC corresponds to a particle, and all the free parameters in RFC are optimally searched. We use the PSO to find a good solution globally, and the incorporation of the Simplex method helps find a better solution around the local region of the best solution found by PSO so far. Then, online adaptive temperature control with ARFC chip implemented by FPGA is proposed. In the ARFC chip, the consequent parameters of all rules are all tuned online using gradient descent. To verify the performance of the ARFC chip, experiments on a water bath temperature system are performed.
Keywords :
adaptive control; evolutionary computation; field programmable gate arrays; fuzzy control; gradient methods; optimisation; temperature control; FPGA; Takagi-Sugeno-Kang fuzzy network; adaptive recurrent fuzzy controller; direct inverse control; evolutionary algorithm; field-programmable gate array; gradient descent; neural fuzzy networks; particle swarm optimization; simplex method; temperature control; water bath temperature system; Adaptive arrays; Adaptive control; Algorithm design and analysis; Evolutionary computation; Field programmable gate arrays; Fuzzy control; Particle swarm optimization; Programmable control; Takagi-Sugeno-Kang model; Temperature control; Direct inverse control; Simplex method; fuzzy chip; fuzzy control; neural fuzzy networks; particle swarm optimization (PSO);
Journal_Title :
Circuits and Systems I: Regular Papers, IEEE Transactions on
DOI :
10.1109/TCSI.2005.854138